Sound enabling computerized system for real time reservoir model calibration using field surveillance data

ABSTRACT

A computer-based system generates digital and audio responses to changes in fluid and rock properties of a producing hydrocarbon reservoir for surveillance analysis. The system calibrates observed changes against directly-measured field data in order to optimize the reservoir model. The changes may include, for example, stress changes in rock, impedance changes in rock, and fluid density changes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to computerized simulation of hydrocarbonreservoirs in the earth, and in particular to reservoir surveillance ofproducing oil and gas fields to monitor and calibrate changes in thesimulated fluid and rock properties of a reservoir.

2. Description of the Related Art

It has been common or conventional to simulate the fluid and rockproperties of subsurface hydrocarbon reservoirs with computerizedmodels. In recent years, a reservoir simulator with massive parallelprocessing capabilities for large scale reservoir simulation wasdeveloped by the assignee of the present application. The reservoirsimulator was known as the POWERS simulator and was described in theliterature. See, for example articles by Dogru, A. H., et al, “AMassively Parallel Reservoir Simulator for Large Scale ReservoirSimulation,” Paper SPE 51886 presented at the 1999 SPE ReservoirSimulation Symposium, Houston Tex., February 1999 and by Dogru, A. H.,Dreiman, W. T., Hemanthkumar, K. and Fung, L. S., “Simulation of Super KBehavior in Ghawar by a Multi-Million Cell Parallel Simulator,” PaperSPE 68066 presented at the Middle East Oil Show, Bahrain, March 2001.

The analysis of multi-million-cell reservoir simulation results has beena relatively new challenge to the petroleum industry. Recently, asdisclosed in commonly-owned U.S. patent application Ser. No. 10/916,851,“A HIGHLY-PARALLEL, IMPLICIT COMPOSITIONAL RESERVOIR SIMULATOR FORMULTI-MILLION CELL MODELS,” filed Aug. 12, 2004 (S. A. Docket No. 503),it has become possible to simulate giant datasets within practical timelimits. With computer power making reservoir size and cell numbers lessof a problem, the capability of human-machine interface to promptlyinteract and discern potential problem areas in the vast amounts of datahas become a concern.

So far as is known, previous efforts have related either to advancedvisualization of three-dimensional data from reservoir simulation or todata-mining approaches in attempts to achieve faster analysis.

Conventional visualization techniques have been generally sufficientwhen the simulation grid blocks have been on the order of some hundredsof thousands. A reservoir engineer's analysis time for datasets of thissize has been comparable with computer processing turnaround time forsimulation results. With multi-million-cell reservoir simulation,however, data analysis has become a significant bottleneck whenconventional monitoring techniques have been used.

Reservoir surveillance of producing oil and gas fields has recentlybecome of interest in the petroleum industry. The intent of reservoirsurveillance has been to gather dynamic measurements which couldpotentially be used to improve management of a producing field, and topossibly optimize recovery of hydrocarbons. Dynamic measurementsindicated changing conditions in the reservoir and were intended toprovide a reservoir engineer with data complementary to the initialstatic or historical information from which reservoir simulation modelswere originally built. So far as is known, previous work in reservoirsurveillance has related to development of equipment for performingfield measurements and to design of surveys to gather data forsurveillance.

Reservoir surveillance or monitoring has, so far as is known, beenaccomplished by acquiring real-time reservoir measurements to augmentour knowledge about the reservoir. The fundamental premise in this dataacquisition has been that dynamic measurements were indicative ofsubstantive changes occurring in the reservoir. As fluids move duringhydrocarbon production, by virtue of water displacing oil or by gasevolving as a gas cap that was previously dissolved in the oil, changesoccur in the intrinsic properties of the reservoir, such as fluiddensity and sonic velocity.

Direct measurement of these changes is an indication of what ishappening inside the reservoir. Present reservoir surveillancetechniques include the following: (a) 4D or time-lapse seismic (repeatedseismic surveying); (b) borehole gravimetry (direct density measurementsat the borehole); (c) microseismic monitoring (sensing ofmicro-earthquakes occurring in the reservoir); and (d) electromagneticresistivity monitoring (measuring electric resistance of reservoirfluids). As reservoir monitoring technologies have been applied in thelast 10 years, it has become apparent that not all reservoirs respondequally well to these direct measurement techniques.

4D time-lapse seismic monitoring relies on the change in seismicamplitude (impedance and reflectivity) as fluids move inside thereservoir. Water displacing oil can have a dimming effect on thebrightness of observed amplitudes. This has proven a useful monitoringtechnique in many fields. But in the case of giant reservoirs, suchdimming may take many years to be observable with precision.Furthermore, this change can only be confidently established in areaswith good seismic signal quality. Many reservoirs in the Middle East,for example, have a number of seismic data quality challenges that make4D seismic of limited applicability and uncertain success.

Borehole gravimetry monitoring relies on observed changes of density atwellbore locations. Water displacing gas represents a very measurabledensity change. Water displacing oil represents a smaller but stillmeasurable density change. In reservoirs with high salinity, however,these differences can be masked.

Microseismic monitoring relies on sensing micro-earthquakes generated bystress changes inside the reservoir. These stress changes occur becausepart of the reservoir rock, under a constant overburden stress, losespore pressure due to fluid production escaping the rock. This increasesthe effective stress (which is the difference between overburdenconfining stress and pore pressure) and the subsequent rock deformationcan produce cracks detectable by seismograms at wellbore stations. Theconsistency of the rock matrix is sometimes too brittle to crack withappreciable tremors, depending on the elastic properties of the rock.

Electromagnetic monitoring relies on measuring formation resistivity.Oil-bearing sands are highly resistive (i.e. low electricalconductivity), whereas water-bearing sands show low resistivity.Depending on the electric properties of the rock, one can relateresistivity change to oil saturation change.

So far as is known, conventional ways to refine or update an existingreservoir model has been by what is known as history matching using wellproduction data from the reservoir. Other data such as that fromreservoir surveillance techniques of the types mentioned was notincluded dynamically into adjustments of the reservoir model. As hasbeen mentioned, time-lapse seismic simulations to indicate postulatedchanges in an existing model have been used, but seismic data does notdirectly relate to fluid or rock properties.

SUMMARY OF THE INVENTION

Briefly, the present invention provides a new and improvedcomputer-implemented method of calibrating a computerized reservoirmodel based on actual reservoir monitoring measurements obtained from asubsurface hydrocarbon reservoir. Numerical predictions are generated inthe computer of reservoir variables in the computerized reservoir modelindicating predicted fluid and rock properties of the reservoir. Thegenerated numerical reservoir variable predictions are then convertedinto predicted sound sequences indicative of the generated numericalreservoir variable predictions. The predicted sound sequences indicativeof the generated numerical change predictions are then stored. Actualreservoir monitoring measurements are converted into actual soundsequences indicative of the actual reservoir monitoring measurements,and the actual sound sequences are stored. An interactive comparison ofthe predicted sound sequences and the actual sound sequences is made ata selected time step to determine if adjustments in the computerizedreservoir model are necessary.

The present invention also provides a data processor which performs theprocessing steps according to the present invention to calibrate acomputerized reservoir model based on actual reservoir monitoringmeasurements obtained from a subsurface hydrocarbon reservoir. Thepresent invention further provides a computer program product in theform of machine-readable instructions for calibrating a computerizedreservoir model based on actual reservoir monitoring measurementsobtained from a subsurface hydrocarbon reservoir to perform theprocessing steps according to the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a process of identification andranking of reservoir monitoring technologies performed in conjunctionwith the present invention.

FIG. 2 is a functional block diagram of a process of integration ofreservoir flow simulation with a petro-elastic model and real-time fielddata according to the present invention.

FIG. 3 is a block diagram of data processing steps according to thepresent invention.

FIG. 4 is a functional block diagram of a computer and associatedperipherals for reservoir surveillance of producing oil and gas fieldsto monitor and calibrate changes in the fluid and rock properties of ahydrocarbon reservoir according to the present invention.

FIGS. 5, 6, 7 and 8 are example display images of reservoir simulationproperties formed according to the present invention.

FIGS. 9, 10, 11, 12 and 13 are example display images of monitoringvariables formed according to the present invention.

FIGS. 14, 15, 16, 17, 18, 19, 20, 21 22, 23, 24 and 25 are example plotsof data indicating wells, subsurface reservoir cells and certainformation fluid or rock properties of such cells obtained according tothe present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the drawings, FIG. 1 illustrates schematically the methodologyaccording to the present invention of evaluating the suitability of oneor more of a number of prospective or candidate reservoir surveillanceor monitoring technologies by simulating the magnitude of the observablereservoir changes. The most commonly used, as has been discussed, are:microseismic monitoring; borehole gravimetry monitoring; time-lapse (or4D) seismic; multi-component seismic; and cross-well electromagnetics.If the magnitude of the monitored changes is substantial enough to bereliably measured, then the monitoring technology is indicated aspractical to be implemented or installed in the reservoir field. Oncethe selected one or more monitoring technologies is implemented,real-time surveillance of the reservoir can proceed.

With this feature of the present invention, no investment in monitoringhardware need be incurred until its benefits have been quantified viamodeling. FIG. 1 schematically illustrates the process of identificationand ranking of candidate reservoir monitoring technologies. In FIG. 1,as indicated schematically at 10, a common earth model of the reservoirserves as the starting point for the identification and monitoring ofcandidate reservoir monitoring technologies. As indicated at 12, basedon prior history a matching prediction of the reservoir underconsideration is made using experience, intuition and other factors. Theresult of the matching prediction is a selected one or more of the typesof reservoir monitoring technologies considered likely to be suitablefor the reservoir under consideration. Next, as indicated at 14, asimulation is performed over a suitable time span of estimated life ofthe reservoir. The simulation is made to determine the magnitude of thereservoir property or properties which are detectable by the selected orcandidate reservoir monitoring technology. The simulation is preferablyperformed according to the techniques of commonly owned U.S. patentapplication Ser. No. 10/916,851, cited above. Then as indicated at 16,the consequences and impact of the candidate monitoring technology arequantified. If a negligible or insignificant change over time isobserved in the reservoir property or properties being simulated, it canbe assumed that any such changes would be obscured over the reservoirlife by measurement errors or repeatability errors. Alternativereservoir monitoring technologies can then be selected as candidates forevaluation according to the procedure shown in FIG. 1. If significantchange is observed, however, the frequency and magnitude of thesimulated changes provide information as to the optimal time intervalsfor measurements to be observed by the candidate monitoring technologyfor maximum benefit.

In the present invention, reservoir simulator data of the type obtainedas disclosed in co-pending, commonly owned U.S. patent application Ser.No. 10/916,851 referenced above is further processed according to apetro-elastic model. The results of the processing sequence are thenused to determine, a-priori, whether it makes sense to obtain 4D seismicdata by simulating the acoustic impedance response of reservoir cellsover time. If a negligible change is observed over time or a smallchange of the order of 5 or 10%, it can be concluded that such changecan easily be masked by measurement errors and repeatability errors. Insuch cases, modeled results from the present invention instead indicatethat alternative surveillance techniques be investigated beforeequipment expenditure and investment need be made on actual 4D seismicsurveys.

On the other hand, when significant impedance change is observed, thepresent invention provides information indicating the optimal timeintervals during which the 4D seismic survey data should be acquired formaximum benefit. Real-time surveillance of the reservoir can thenproceed. Thus, as noted, no investment in monitoring hardware need beincurred until its benefits have been quantified via modeling.

With the present invention, the simulator is further used to predict themagnitude of change of subsurface reservoir bulk density properties atdifferent well locations. The bulk density surveillance results indicatewhether borehole gravimetry surveillance should be applied in a givenreservoir. The data also would indicate what well locations offer themost useful borehole gravimetry surveillance.

According to the present invention, the simulator also predicts themagnitude of changes in reservoir stress due to fluid production orother causes at different well locations. The predicted stress andrelated fracture and subsidence data indicate whether a microseismicmonitoring surveillance technique should be applied in a givenreservoir. The data also indicate what well locations offer the mostuseful microseismic surveillance sites if the technique is indicated tobe desirable or feasible.

With the present invention, the simulator is further used to predict themagnitude of change of formation resistivity derived from changes information saturation. The formation resistivity predictions indicatewhether cross-well electromagnetic monitoring should be applied in agiven reservoir. The data also would indicate which well locations offerthe most suitable sites for cross-well electromagnetic surveillance.

Once one or more of these monitoring technologies shows a favorableranking and has been implemented, the present invention permits thereservoir engineer to evaluate the field measurements by comparing themwith the simulated response. The engineer may adjust the frequency ofdata acquisition based on the rate of change of the monitored propertythat the simulation has predicted.

The present invention also provides validation of the geological modelitself since the simulator can generate seismic information at time zero(i.e. before any production) to correlate with the seismic data used tobuild the geological model. After some calibration, the engineer may beable to advance the simulation synchronously with the fieldmeasurements. FIG. 2 illustrates schematically the integration ofreservoir flow simulation with the petro-elastic model and real-timefield data.

As shown in FIG. 2, an existing model of the reservoir underconsideration based on seismic data and well data is initially presentin the form of a simulation database as shown at 20. The reservoirdatabase is then processed as indicated at 22 to a suitable flowmodeling process. A preferred such modeling process is that described indisclosed in co-pending, commonly owned U.S. patent application Ser. No.10/916,851 referenced above. It should be understood that other flowmodeling computer processes could be used as well. The results of theflow modeling process are certain properties of the reservoir such aspressure, fluid saturation, fluid density and the like formationproperties in the cells of the reservoir over its projected productionlife.

Next, as shown at 24, a petro-elastic modeling computer process isperformed, as will be described below. The results of the petro-elasticmodeling are then compared during a real-time filtering step 26 withfield measurements obtained by selected monitoring surveillancetechniques.

Processing of data according to the present invention may be performedin a number of computer platforms. For example, the processing may beperformed in a reservoir simulator of the type disclosed in co-pending,commonly owned U.S. patent application Ser. No. 10/916,851, “AHIGHLY-PARALLEL, IMPLICIT COMPOSITIONAL RESERVOIR SIMULATOR FORMULTI-MILLION CELL MODELS” as mentioned above. The present invention mayalso be implemented in conjunction with a mixed paradigm parallel(combination of shared memory parallel and massively parallel) reservoirsimulator, as well as other paradigms for parallel reservoir simulation.

The processor of the computer as shown schematically at 30 (FIG. 4)receives the data concerning the reservoir of interest to undertake thelogic of the present invention, which may be executed by a processor asa series of computer-executable instructions. The data concerning thereservoir of interest is provided from the simulator database formedduring steps 20 and 22 shown in FIG. 2 and described herein. Theinstructions may be contained on a data storage device 32 with acomputer readable medium, as shown, having a computer usable mediumstored thereon. Or, the instructions may be stored in memory of thecomputer 30, or on magnetic tape, conventional hard disk drive,electronic read-only memory, optical storage device, or otherappropriate data storage device. The results of the processing are thenavailable on a video/audio display as shown at 34 or printer or anyother form of output device.

The flow chart of FIG. 3 illustrates the structure of the logic of thepresent invention as embodied in computer program software. Thoseskilled in the art will appreciate that the flow charts illustrate thestructures of computer program code elements including logic circuits onan integrated circuit that function according to this invention.Manifestly, the invention is practiced in its essential embodiment by amachine component that renders the program code elements in a form thatinstructs a digital processing apparatus (that is, a computer) toperform a sequence of function steps corresponding to those shown.

It is important to note that, while the present invention has been, andwill continue to be, described in the context of a fully functionalcomputer system, those skilled in the art will appreciate that thepresent invention is capable of being distributed as a program productin a variety of forms, and that the present invention applies equallyregardless of the particular type of signal-bearing media utilized toactually carry out the distribution. Examples of signal-bearing mediainclude: recordable-type media, such as floppy disks, hard disk drives,and CD ROMs, and transmission-type media such as digital and analogcommunication links.

It should be understood that the processing described herein can beimplemented in a variety of other types of reservoir simulators. It canbe run on a variety of computer platforms, such as single CPU, a sharedmemory parallel or massively parallel processing computer, a distributedmemory super-computer, and a variety of PC clusters, such as a self-madePC cluster, or a production PC cluster.

A schematic flow chart of the processing according to the presentinvention for interactive analysis of simulation results is shown inFIG. 3. The process of FIG. 3 is a computer-implemented method ofcalibrating a computerized reservoir model based on actual reservoirmonitoring measurements obtained from a subsurface hydrocarbonreservoir. The process of FIG. 3 is performed in real-time asdirectly-measured field data is obtained from the continuous measurementtechnology which is monitoring performance of the subsurface hydrocarbonreservoir during actual production. As will be set forth, digital andaudio responses are generated as a result of changes in the reservoir'sfluid and rock properties as predicted by the reservoir model, and thechanges so generated are calibrated against the directly-measured fielddata. The results of the calibration according to the present inventionpermit adjustment and optimization of the reservoir model based onreal-time data. As will be set forth, changes generated in the reservoirmodel can take a number of forms, for example: stress changes in rock(which can be correlated with passive micro-seismic measurements);impedance changes in rock (which can be correlated with either 4D orrepeated 3D seismic measurements); and fluid density changes (which canbe correlated with direct borehole gravimetry measurements).

The process of FIG. 3 is performed interactively by a reservoir engineerin conjunction with the computer 30 (FIG. 4) and in connection with thecomputer-implemented petro-elastic modeling process is being performedas indicated at 26 in FIG. 2. In the process illustrated in FIG. 2, aflow chart F indicates a sequence of processing steps according to thepresent invention. A step 40 begins the process by reading the dataconcerning the reservoir model or some portion thereof which is ofinterest for a particular time-step from the simulator database 20.Next, as indicated at step 24, changes in the data of the reservoirmodel from a previous time-step are determined. The data values obtainedduring step 24 are then converted to sound values during step 42 in asuitable conversion device or mechanism, such as a MIDI sequencer of theconventional type.

The sounds or audible signals formed from reservoir data variablesduring conversion step 42 are preferably based on MIDI musical scales.For example, a specific variable can be assigned to audit representationby a specific musical instrument, a range of notes and a pitch orvolume, or some combination or variation thereof.

A more specific example, field pressure depletion can be represented byassigning an increasing sound pitch to indicate a corresponding loss ofpore pressure and accompanying increase in effective stress. Anotherexample is pressure change on an individual well being represented apitch or tone ramping up progressively as pressure increases.

The computed reservoir model changes resulting from step 24 may also, ifdesired, be subjected to an event monitoring system as will be describedbelow. The digital data values resulting from step 24 and the valuesresulting from sound conversion step 42 are then stored in a temporarylog file during step 44.

Concurrently with the performance of steps 20, 40, 24 and 42, and inreal-time directly-measured field data is obtained as indicated in step41 from the continuous measurement technology which is monitoringperformance of the subsurface hydrocarbon reservoir during actualproduction. The actual directly-measured data values obtained duringstep 41 are then converted to sound values during step 43 in a suitableconversion device or mechanism, such as a MIDI sequencer of theconventional type, as is the case in step 42 for reservoir model changedata. The digital data values resulting from step 41 and the valuesresulting from sound conversion step 43 are then stored in a temporaryfield data file during step 45.

The reservoir change data from log file 44 and the temporary field datafrom file 45 are then subjected to cross-correlation step 26, whichincludes a time-shift comparison step 46 and a magnitude equalizationstep 48. The results of cross-correlation step 26 are then examined bythe reservoir engineer interactively during step 50.

During interactive examination, the reservoir engineer may monitorevents based on state of the art reservoir surveillance, where manymeasurements other than pressure are acquired in real-time. Thepetro-elastic model is used to compute seismic and stress responses thatcan be compared with measured variables.

Additionally, the reservoir engineer may try to monitor events onsimulation variables such as pressure, saturation and mole-fractioncompositions from a reservoir simulator output, triggering event alarmsaccordingly (e.g. dew point pressure, condensate dropout, high H₂Sconcentration, etc.) and using field-measured pressure to validate theirmatch. For such specific use which has previously been the traditionalrole of reservoir simulation, engineers do not need to invoke thepetro-elastic model to generate stress, seismic or any othersurveillance-related information, since no correspondence surveillanceequipment is installed.

Based on the reservoir engineer's decision during step, a step 52 may beperformed during which the present version of the reservoir model may beadjusted as to parameters or values of monitoring variables in thereservoir model. The adjusted or changed reservoir model values are thenstored in the simulator database 20. Alternatively during step 52, thetime-step may be adjusted during a step 54, and the process returns tostep 40 for further processing for the new time-step in the mannerpreviously described. A further alternative during decision step 52 isto exit as indicated at 55 from further processing.

Nomenclature of Variables

Set forth below for ease of reference and understanding is a listing ofthe nomenclature used in the Equations which express the physicalrelationships between the various parameters and measurements used indata processing steps and analysis according to the present invention:

c_(o)=Oil compressibility

c_(g)=Gas compressibility

c_(w)=Water compressibility

C_(φ)=Layer compressibility

D=Reservoir depth

E=Young Modulus

G=Shear modulus

G_(dry)=Dry rock shear modulus

h=Reservoir layer thickness

K=Bulk modulus

K_(dry)=Dry rock bulk modulus

K_(f)=Fluid bulk modulus

K_(m)=Matrix bulk modulus

K_(t)=Estimated Gain of the Kalman Filter

K_(u,t)=Updated Gain of the Kalman Filter

m=Archie's cementation factor

n=Archie's saturation exponent

P=Pore Pressure

R_(p)=P-Wave reflectivity

R_(s)=S-Wave reflectivity

R_(t)=True formation resistivity

R_(w)=Formation water resistivity

S_(g)=Gas saturation

S_(o)=Oil saturation

S_(w)=Water saturation

V_(p)=P-Wave (acoustic) velocity

V_(s)=S-Wave (shear) velocity

x_(t)=Noisy measurement input to Kalman Filter

Z_(p)=P-Wave (acoustic) impedance

Z_(s)=S-Wave (shear) impedance

Greek Symbols:

α=Biot's Parameter=

φ=Porosity

γ=Fracture Gradient

ν=Poisson's Ratio

ρ_(B)=Bulk density

ρ_(f)=Fluid density

ρ_(g)=Gas density

ρ_(m)=Matrix (rock) density

ρ_(o)=Oil density

ρ_(w)=Water density

σ_(H)=Horizontal stress

σ_(V)=Vertical stress

σ_(n)=Standard deviation of noisy measurement

σ_(t)=Estimated standard deviation of Kalman Filter

σ_(u,t)=Updated standard deviation of Kalman Filter

Petro-Elastic Model

Mathematical computerized models added to the reservoir simulatorprovide the following information useful in both monitoring technologyselection/ranking and in the real-time surveillance process:

Fluid modulus, fluid density and bulk density are given by the equationsset forth below. Equation  1:$K_{f} = \frac{1}{\left\lbrack {{c_{w}S_{w}} + {c_{o}S_{o}} + {c_{g}S_{g}}} \right\rbrack}$Equation  2: ρ_(f) = ρ_(w)S_(w) + ρ_(o)S_(o) + ρ_(g)S_(g) Equation  3:ρ_(B) = ρ_(m)(1 − ϕ) + ρ_(f)ϕ

These quantities are used inside the simulator to compute the saturatedbulk and shear moduli of the rock at every grid cell using thewell-known Gassmann's equations. Equation  4:$K = {K_{dry} + \frac{\left( {1 - \frac{K_{dry}}{K_{m}}} \right)^{2}}{\frac{\phi}{K_{f}} + \frac{1 - \phi}{K_{m}} - \frac{K_{dry}}{K_{m}^{2}}}}$Equation  5: G = G_(dry)

The values of K and G are used to compute petro-elastic P-Wave andS-Wave velocities inside the simulator: Equation  6:$V_{P} = \sqrt{\frac{K + {\frac{4}{3}G}}{\rho_{B}}}$ Equation  7:$V_{S} = \sqrt{\frac{G}{\rho_{B}}}$

These velocities are then used to compute simulator-generated values forseismic impedance for both P and S waves:Z _(P)=ρ_(B) V _(P)  Equation 8Z _(S)=ρ_(B) V _(S)  Equation 9

These impedances are then used to generate seismic reflectivities (i.e.seismograms) from the simulation itself: Equation  10:$R_{P} = \frac{Z_{{P\quad 2} -}Z_{P\quad 1}}{Z_{P\quad 2} + Z_{P\quad 1}}$Equation  11:$R_{s} = \frac{Z_{S\quad 2} - Z_{S\quad 1}}{Z_{S\quad 2} + S_{S\quad 1}}$

P and S-Wave velocities are also used to compute the so-called Poisson'sRatio: Equation  12:$v = \frac{V_{s}^{2} - {0.5V_{p}^{2}}}{V_{s}^{2} - V_{p}^{2}}$

The Poisson's Ratio is then used to estimate several rock-mechanicalstress-related quantities, such as Young modulus (which can be computedfrom either K or G):E=3K(1−2ν)=2(1+ν)G  Equation 13and horizontal stress: Equation  14:$\sigma_{H} = {{\frac{v}{1 - v}\sigma_{V}} + {\frac{1 - {2v}}{1 - v}\alpha\quad P}}$

and fracture gradient: Equation  15:$\gamma = {\frac{v}{1 - v}\frac{\sigma_{V}}{D}}$

and uni-axial compaction (more commonly known as “subsidence”):Equation  16:$\frac{\Delta\quad h}{h} = {\frac{1}{3}\frac{1 + v}{1 - v}\phi\quad C_{\phi}\Delta\quad P}$

True formation resistivity is also computed, using the water saturationgenerated by the simulator and reservoir porosity, to assist reservoirmonitoring via cross-well electromagnetics:R _(t) =R _(w)φ^(−m) S _(w) ^(−n)  Equation 17

Equations 1 through 17 comprise the “Petro-Elastic Model” for monitoringtechnology ranking and surveillance, which is coupled as disclosed abovewith the reservoir simulator. Each property is evaluated on acell-by-cell basis. This means that an entire 3D volume of data can begenerated for each of these properties and compared to actualmeasurements of these quantities at any simulation time.

Reservoir Property Displays

FIGS. 5 through 8 are images of display screens of certain propertiesfor a subsurface hydrocarbon reservoir at a selected depth or region inthe reservoir during a given time step obtained according to the presentinvention. FIG. 5 is an image of pressure P obtained from the flowsimulation process. FIG. 6 is an image of gas saturation S_(g) similarlyobtained from the flow simulation process. FIG. 7 is an image of thepressure between layers in the reservoir similarly obtained from theflow simulation process.

FIG. 8 is a self-organizing map of data obtained according to thepresent invention. A self-organizing map is a fairly traditionaldata-mining technique which combines multiple reservoir properties andcondenses them into a single output volume of “classes”. For example,reservoir porosity and permeabilities, horizontal and vertical, can beclassified into “rock type” classes. This reduces the dimensionality ofthe dataset, meaning less data to handle, but masks the individualinformation of the reservoir properties that the engineer may need. Forexample, rocks with high porosity and high permeability can be made toappear in a class by themselves (i.e., “optimal flow” class) and lowporosity and low permeability would appear in another distinct class(i.e., “poor rock quality” class). However, the individual informationof quantitative value (percent p.u. for porosity or millidarcies forpermeability) would not be readily available. The self-organizing mapdata such as that shown in FIG. 8 can be analyzed through the eventmonitor process if the engineer so desires.

It should be understood that other data-mining or data condensationtechniques can be used as well. Clustering analysis is another techniquelike self-organizing maps which groups the information into clusters(another name for “classes”) for quick qualitative analysis at theexpense of quantitative detail. Clustering analysis looks for thedominant trends in the data, i.e. highlighting the clusters with highestand lowest number of members in their class.

Calibration of the model is necessary when the event measured and theevents recorded are shifted in time such as, for example, when a densitychange due to water front movement happened much earlier (or later) inthe model than verified by borehole density measurements. By “muchearlier or later” it is meant a time lag that cannot simply beattributed to measurement latency, which is easily corrected byfiltering. It is important to note that mere differences in magnitudebetween the variables modeled and measured do not usually requireadjustment: it is the relative change that matters. This is natural toexpect because seismic reflectivity generated by the simulator, forexample, does not have the same amplitude of field-recorded seismicdata. This should be adjusted by just applying a simple “gain” to one ofthe measurements (for which seismic information generated by thesimulator at time zero, both impedance and reflectivity, is mostvaluable).

In this sense, it is useful to compute the “percent change” of anyvariable as an indicator. For the measured variables one would compute(measured(t+dt)−measured(t))/measured(t)*100, where t and t+dt indicatea reference time step and a later time step respectively. Similarly, forthe modeled variables one would compute(modeled(t+dt)−modeled(t))/modeled(t)*100. If these two computationsagree closely there is no need for correction (a 5% discrepancy can beconsidered normal in many cases, even a 10% discrepancy could occur dueto rapidly varying environment conditions, such as temperature changesbetween day and night in Middle East deserts in the summer season).

Event Monitor Construct

As has been noted, FIGS. 5-8 are displays of individual properties orfeatures at a selected depth or region in a reservoir during aparticular time step. A number of reservoirs, however, are known to becomposed of multiple millions of three-dimensional cells and a projectedreservoir life of a significant number of years. The amount of datacontained in these 3D volumes of each variable can be overwhelming asthe reservoir simulation model may thus contain tens of millions of datavariables to be displayed. The task of analyzing all this data may seemdaunting at first but the computer 30 performs the search of anyrelevant features using an event monitor construct, as will bedescribed. Using BNF (Backus-Naur Form) syntax, the engineer describessimple rules of what to search for. The computer program parses theserules and searches each cell for the variables involved in the rules.

With the present invention, an event monitor construct is provided forsearching the simulation data for relevant features of interest to thereservoir engineer. The event monitor may apply the syntax rules to allwells involved in reservoir production, to a subset of wells in thereservoir model, to specific wells only or to all reservoir cells (or arange/subset of those cells). The event monitor may apply the syntaxrules to all wells involved in reservoir production, to a subset ofwells in the reservoir model, or to specific wells only. Combinations ofthese modes can be used in a set of rules for a single event monitorconstruct, if desired.

This event monitor construct provides a new and improved form of dataanalysis that complements three-dimensional (3-D) analysis of data suchas that shown in the displays of FIGS. 5 through 8. A set of syntaxrules prescribed by a reservoir engineer or analyst automatically findspatterns of interest in the data relating to reservoir conditions. Thesyntax rules may be applied to primary properties (FIGS. 5-7) of thereservoir as well as to data-mined byproducts (FIG. 8) of theseproperties, such as self-organizing maps, K-means clustering and thelike. The engineer can be alerted to conditions of interest occurring atany location of interest in the reservoir. Example conditions include:condensate dropout at wells; sour gas migration; reservoir pressureapproaching bubble point or dew point conditions; rapid fluid saturationchanges; relative impedance changes; reservoir compaction changes andthe like.

The reservoir pressure displayed in FIG. 5 is of special practicalimportance to the engineer because any pressure at a well lower than thedew-point-pressure will result in “condensate drop-out”, which is theformation of condensate in the well. This reduces the gas flowperformance and is, therefore, a condition the engineer must monitorcarefully. Such an event would be monitored as follows (for a gasreservoir where the dew-point pressure is 6000 psi):

Event

CloseToDewPointPressure Always

At_Well(All_Wells, 1)

Find_All Where ((PRESSURE<6000.0) AND (SGAS>0.1))

Sound Talk(“Pressure below 6000 at well”,Well_Name)

EndEvent

Once significant condensate-dropout has occurred, another rule can trackthe amount of oil saturation in the condensate bank (high oil saturationwill inhibit gas flow altogether) so that the engineer may decide toshut-down that particular well. The engineer will also look at the gassaturation display (FIG. 6) in this context:

Event

WellDropout1 Always

At_Well(All_Wells, 1)

Find_All Where (SOIL>0.15)

Sound Talk(“Condensate Dropout at Well”, Well_Name)

Graphics Opacity(0)

EndEvent

Instead of monitoring this condition at the wells, the engineer can alsomonitor throughout the reservoir:

Event

CondensateDropout1 Always

Find_All Where (SOIL>0.15)

Sound Talk(“Condensate Dropout at grid block”, Cell_Location)

Graphics Opacity(0)

EndEvent

The pressure display between layers (FIG. 7) will enable a reservoirengineer to see how many layers in the reservoir (or well perforationsin the well) are being affected by condensate dropout in order to make amore informed decision as to the progress of this condensate banking andthe need to close well perforations.

Another important operational situation of hydrogen sulfide (H₂S)migration in reservoirs due to well production using the following:

Event

HighH2S Always

Find_All Where (ZMF2>0.0001)

Sound Talk(“H2S Concentration High at grid block”, Cell_Location)

EndEvent

This rule monitors any mole fraction of H₂S that exceeds 0.0001. Notonly is hydrogen sulfide corrosive to the well internals but also lowersthe market value of the gas produced (because sour gas requires extrarefining/processing steps to bring to market).

The following example monitors the occurrence of a positive gradient inP-impedance (also known as acoustic impedance):

Event

Impedance1 Always

Find_All Where (Gradient P_IMPEDA>0.0)

Sound Talk(“Positive Impedance Gradient at grid block”, Cell_Location)

EndEvent

A positive gradient means that a fluid with lower density has beendisplaced by one with higher density. This is a typical case of watersweeping oil due to water injection to increase oil recovery and it isbeneficial to know when and where this fluid displacement has occurredin the reservoir.

Similarly, the following example monitors the occurrence of a negativegradient in P-impedance or acoustic impedance:

Event

Impedance2 Always

Find_All Where (Gradient P_IMPEDA<0)

Sound Talk(“Negative Impedance Gradient at grid block”, Cell_Location)

EndEvent

A negative gradient means that a fluid with higher density has beendisplaced by one with lower density. This is a typical case of secondarygas-cap formation, where gas comes out of its solution in oil to form adistinct thermodynamic phase as a gas dome on top of the oil reservoir.This is an effect of pressure decline in the reservoir and it isimportant to know when and where this has occurred in the reservoir.

The following describes the syntactic structure of the event monitorconstruct. To avoid ambiguity and verbose description, the syntax ofthis construct is presented using Backus Naur Form (BNF) Grammar. BNF isused because it is the most common notation used to express context-freegrammars. A context-free grammar is a set of recursive rewriting rules,also termed productions, used to generate patterns of strings that canbe easily parsed for further analysis. The recursiveness or ability tocall itself of a context-free grammar makes it specifically adapted tobuild rules of arbitrarily complex logic while maintaining a structurethat is easy to parse.

Each construct in the event monitor is chosen to find a particular eventof interest in the data and perform a set of actions once the monitoredevent is detected. The body of the construct set forth below is mainlycomposed of two parts; a condition part and an action part. Thecondition part lists an expression that tests the occurrence of theevent, while the action part describes what actions to be taken once anevent is detected.

BNF Grammar

The BNF grammar is a formal notation used to describe the syntax of alanguage or language construct. It is composed of a number ofproductions. Each production describes the structure of itsleft-hand-side component by the right-hand-side component.

The left-hand-side of a production contains a single componentclassified as a non-terminal entity, while the right-hand-side iscomposed of one or more components that can be either terminal ornon-terminal entities. Each non-terminal entity must be furtherdescribed by appearing as a left-hand-side of some production. Theterminal entities constitute the strings that can be used to form eachacceptable construct of the described language. The fact that theleft-hand-side is described by the right-hand-side does not excluderecursive definitions as long as the right-hand-side has an alternatethat can be used to terminate the recursion.

Notation Description

The following points are provided to help in clarifying the notationused in the grammar:

The string “::=” meaning “is defined as”

The character ‘|’ means “or”

Optional components are enclosed in square brackets (‘[’, ‘]’).

The following ‘<’, ‘>’, ‘=’, ‘< >’, ‘>=’, ‘<=’, ‘:’ are terminaloperators.

The string “STRING” denotes a quoted string and is considered a terminalentity.

All other strings in the grammar are non-terminals.

Each production is preceded by a reference number that is used only toreference the particular production.

The Grammar

The preferred BNF grammar used with the present invention is as follows: 1) Event_Monitor_List ::= Event_Monitor_Statement  1)Event_Monitor_List ::= Event_Monitor_Statement Event_Monitor_List  2) |[Event_Monitor_Statement ]  3) Event_Monitor_Statement ::= EventEvent_Name Event_Frequency Event_Body EndEvent  4) Event_Name ::=IDENTIFIER  5) Event_Frequency ::= Once  6) | Always  7) Event_Body ::=[ AtTimestep_Statement ] [ AtLayer_Statement ] [ AtWell_Statement ]Event_Expression Event_Clause_List  8) AtTimestep_Statement ::=At_TimeStep ( IndexSpecifier )  9) AtLayer_Statement ::= At_Layer (IndexSpecifier ) 10) AtWell_Statement ::= At_Well ( Well_Specifier ,Number ) 11) Well_Specifier ::= STRING 12) | All_Wells 13)Event_Expression ::= Search_Scope Where ( Event_Condition_List ) 14)Search_Scope ::= Find_All 15) | Find_Any 16) Event_Condition_List::= Or_Condition OR Event_Condition_List 17) | Or_Condition 18)OR_Condition ::= And_Condition AND OR_Condition 19) | And_Condition 20)And_Condition ::= NOT ( And_Condition ) 21) | ( And_Condition ) 22)| Simple_Condition 23) Simple_Condition ::= ( Gradient PropertyName[ElementSpecifier] CompOpr Value) 24) | ( PropertyName[ElementSpecifier] CompOpr Value) 25) ElementSpecifier ::= (IndexSpecifier , IndexSpecifier , IndexSpecifier ) 26) IndexSpecifier::= IndexRange 27) | Number 28) CompOpr ::= > | < | = | >= | <= | <> 29)Event_Clause_List ::= Event_Clause Event_Clause_List 30) | Event_Clause31) Event_Clause ::= Sound_Clause 32) | Haptic_Clause 33) |Graphics_Clause 34) | Message_Clause 35) Sound_Clause ::= Sound Play (filename ) 36) | Sound Talk ( STRING , Event_Location_Info ) 37)Haptic_Clause ::= 38) Graphics_Clause ::= Graphics Opacity ( Number )39) Message_Clause ::= Message ( STRING , Event_Location_Info ) 40)Event_Location_Info::= Cell_Location 41) | Well_Name 42) |Cell_and_Well_Info 43) | None 44) IndexRange ::= Number : Number 45) | :46) Number ::= Integer 47) Value ::= Real Number 48) filename ::= STRING

Non-Terminals

The following is a list of all the non-terminal entities that appear inthe proposed grammar:

And_Condition

AtLayer_Statement

AtTimestep_Statement

AtWell_Statement

CompOpr

ElementSpecifier

Event_Body

Event_Clause

Event_Clause_List

Event_Condition_List

Event_Expression

Event_Frequency

Event_Location_Info

Event_Monitor_List

Event_Monitor_Statement

Event_Name

Filename

Graphics_Clause

Haptic_Clause

IndexRange

IndexSpecifier

Message_Clause

Number

OR_Condition

Search_Scope

Simple_Condition

Sound_Clause

Value

Well_Specifier

Terminals (Keywords and Operators)

The following is a list of all the terminal entities (both keywords andoperators) that appear in the proposed grammar:

-   -   All_Wells    -   Always    -   AND    -   At_Layer    -   At_TimeStep    -   At_Well    -   Cell_and_Well_Info    -   Cell_Location    -   EndEvent    -   Event    -   Find_All    -   Find_Any    -   Gradient    -   Graphics    -   Message    -   None    -   NOT    -   Once    -   Opacity    -   OR    -   Play    -   Sound    -   Talk    -   Well_Name    -   Where    -   IDENTIFIER: Any Sequence of characters and/or numbers starting        with a character    -   ‘>’, ‘<’, ‘=’, ‘>=’, ‘<=’, ‘< >’, ‘:’    -   Integer Numbers    -   Real Numbers

STRING: Any quoted string

Notes on the Semantics of the Event Monitor Grammar

The event monitor syntax set forth above provides an engineer with atool to automatically perform interpretation and analysis of reservoirsimulation results. The syntax enables the engineer to define a seriesof rules to quickly interpret data. These rules can be stored andre-applied to updated simulation runs of the same model or serve astemplate for new simulations on different models. It also provides aknowledge capture capability that engineers can invoke to ease theiranalysis and give them an understanding of what other engineers havelooked for in simulation results.

The immediate use of this Event Monitor is for timestep-by-timestepanalysis of simulation results. But exactly the same syntax will be usedin more advanced applications, such as:

(a) Scan the entire simulation result for all timesteps in “batch mode”and generate a log of all events occurring; or

(b) Work interactively with an on-line (real-time) simulation as ithappens.

The Event Monitor grammar productions have been numbered sequentially.The following discussion uses that numbering to explain the lessself-apparent entries.

(5, 6): Event Frequency.

The keyword “Once” implies that, once the event happens and triggers aresponse during a timestep, it will not trigger a response again at alater timestep. The rationale was to avoid undesirable repetitivemessages, sounds, etc. when the engineer is already aware that themonitored condition has occurred.

The keyword “Always” implies that the response will be triggeredwhenever the event happens regardless of whether it has happened beforeor not.

(8): At_Timestep (IndexRange).

This keyword indicates that the event will be monitored only at thetimesteps indicated by a range. The rationale is to provide the engineerwith a means to monitor only a portion of the simulation (simulationhistory period or simulation prediction period, for example).

Example:

At_Timestep(5:10) indicates that the event will be monitored only fortimesteps 5,6,7,8,9 and 10.

(9): At_Layer (IndexRange).

This keyword indicates that the event will be monitored only for thereservoir layers indicated by a range. The rationale is to provide theengineer for a way to concentrate on a vertical region of the reservoironly, such as a gas cap, the oil window or below the oil-water-contact.

Example:

At_Layer (1:10) indicates that the event will be monitored only forlayers 1 through 10 inclusive.

(10,11,12): At_Well(Well_Specifier, Number).

This keyword indicates that the event will be monitored only along aspecified well or wells. This not only includes the wellbore cell butalso a radius around it. The rationale is to provide the engineer with atool that will pinpoint important changes at and near the wells as thesimulation proceeds.

Examples:

At_Well (“Gusher-2”,3) means that the cells perforated by well Gusher-2and 3 cells in a radius around it will be monitored and the rest of thereservoir ignored.

At_Well (All_Wells,3) will monitor the event at all wells without theneed for exhaustive enumeration.

At_Well (All_Wells,0) will monitor the event at all wells but only atthe cells perforated by the well, without any search radius around it.

(13,14,15): Search_Scope Where (Event_Condition List).

This production indicates that the event will be monitored only wherethe event condition holds. In the Search_Scope, the keyword Find_Anymeans that the action is to be triggered upon its very first occurrencein any cell within the condition search scope, without exploring theremaining cells. The keyword Find_All implies the opposite, i.e., allcells must be inspected and tagged accordingly before any action istriggered. The rationale is to provide a choice of either immediatelocalized warnings or a map of geobodies formed by the cells that havesatisfied this condition.

(23,24): (Gradient PropertyName).

This is a special type of condition where it is not the value of theproperty itself that is monitored but its gradient, that is, thedifference between its current value and its value at the previoustimestep. The rationale is to provide the engineers with the capabilityto monitor a “rate of change” as to advise them of rapidly changingconditions in the reservoir.

(35): Sound Play (filename).

triggered action currently used, where a triggered action impliesplaying sound from a .wav or .mid file.

(36): Sound Talk (STRING, Event_Location_Info).

This clause will generate voice out of text message built using theprovided arguments. The arguments are as follows:

STRING is an arbitrary user defined text string, and Event_Location_Infowill dictate what event location information should be appended to thetext string.

Examples:

Sound Talk(“Event condensate_dropout at cell”,Cell_Location)

This would speak out a message like “Event condensate dropout at cell14,20,5”

Sound Talk(“Event condensate_dropout at well”,Well_Name)

This would speak out a message like “Event condensate dropout at wellProducer-15”

(37): Haptic_Clause is available for possible use as a triggered action.

(38): Graphics Opacity (Number).

This triggered action uses graphics instead of sound. A translucentopacity number (in the argument Number) will be applied to all cells inthe reservoir to fade away except those that satisfied the condition,which should be revealed at full strength. The rationale is to directthe attention of the engineer to the specific areas that satisfy thecondition so that they are clearly visible in the 3D display, witheverything else being visibly diminished or fading into an imperceptiblebackground.

(39): Message (STRING, Event_Location_Info).

This is analogous to production (36) except that, instead of sound, apop-up window on the computer display becomes the triggered action. Thiswindow has a dismiss feature or button or similar functionality to beclicked indicating that the engineer has acknowledged the message.

Example:

Message (“Warning: Event water_breakthrough at cell”,Cell_Location)

This would bring up a message box with the text “Warning: Eventwater_breakthrough at cell 30,40,20”

Using the BNF rule syntax as described above, an engineer can query thestate of any of reservoir surveillance variables inside the simulator atany time. FIGS. 9 through 13 are displays of reservoir surveillancevariables obtained with the present invention. Examples of BNF syntaxrules for the variables shown in these Figures and obtained in thecomputer as described in the Equations 1-22 as applicable above follow:

Event

Horizontal_Stress Always

Find_All Where (Gradient H_STRESS<0.0)

Sound Talk(“Negative Stress Gradient At grid block”, Cell_Location)

EndEvent

Event

Uniaxial_Compaction Always

Find_All Where (UNI_COMP>2.0)

Sound Talk(“Uniaxial Compaction greater than 2 at grid block”,Cell_Location)

Graphics Opacity(0)

EndEvent

Event

Bulk_Density Always

Find_All Where (BULKDENS<2.0)

Sound Talk(“Density less than 2 at grid block”, Cell_Location)

EndEvent

Event

SOM Always

Find_All Where (SELFMAP<2.0)

Sound Talk(“Self Organizing Map Cluster less than 2 at grid block”,Cell_Location)

Graphics Opacity(0)

EndEvent

Event

Impedance1 Always

Find_All Where (Gradient P_IMPEDA>5000.0)

Sound Talk(“Positive Impedance Gradient at grid block”, Cell_Location)

EndEvent

Event

Impedance2 Always

Find_All Where (Gradient P_IMPEDA<0)

Sound Talk(“Negative Impedance Gradient at grid block”, Cell_Location)

EndEvent

In the above examples the “Sound Talk” command passes the text stringand the vector containing the cell location to a Text-To-Speechinterface such as that provided by Microsoft Corporation, so that asystem voice reads the message aloud. The use of voice alerts isparticularly helpful during calibration as shown in FIG. 3, since thevoice message does not alter the visual display or any directinteractions of the engineer with the data. Microsoft Corporation offersseveral voice types in any Windows® XP installation. The ones foundclearest for use have been those known as “Michael” (male voice) and“Michelle” (female voice).

True speech or voice alerts formed in response to the event monitoringis preferably machine-generated human-like voice. It could also bepre-recorded voice messages based on the alert to be given if desired.The sound representations converted from reservoir variables aredifferent and are based on MIDI musical scales and are thus conceptuallyand audibly different from human-like voice messages.

Kalman Filtering

The reservoir simulator time step in FIG. 3 is controlled by thereservoir engineers, who can stop/pause the simulator at a given pointin time or ask it to advance to the next time step. The field dataacquired during surveillance typically has two main characteristics thatthe engineer is very likely to encounter: the field data will be noisy,or the field data will not arrive at the precise time-step of thesimulation.

The first issue is well known in signal processing and the solution hereis to apply during step 26 (FIG. 2) a fast-compute filter. Preferably,Kalman Filtering is selected, which has an added advantage because suchfiltering works as a running least-squares smoother and therefore can beupdated as new data arrives without having to re-compute the entirefiltered sequence for each new data point.

In the process of reservoir surveillance, it is necessary to estimate,via measurements, the state of a reservoir variable and its uncertainty.However, it is not necessary to directly observe these states. It isnecessary only to observe some measurements from an array of sensors,which are noisy. As an additional complication, the states evolve intime, also with its own noise or uncertainties. The Kalman Filter isused to address the question of how one can optimally use themeasurements of unobserved variables and their uncertainties.

The power of the Kalman Filter is that it operates on-line. This impliesthat, to compute the best estimate of a state and its uncertainty, theprevious estimates can be updated by the new measurement. Therefore, itis not necessary to consider all the previous data again. In order tocompute the optimal estimates, rather, one only needs to consider theestimate from the previous time step and the new measurement. The KalmanFilter is a known computer-implemented signal processing techniquewidely discussed in the literature, so only those Kalman filteringfeatures as applied in the present invention are discussed herein.

Given an initial noisy data measurement x₀ from a monitoring instrument,that measurement is used as a first estimate y₀ of the correct value.The error variance of the instrument (or its square root, which is thestandard deviation of the error σ_(n)) is also known. A value can alsobe assumed for the initial variance of the estimate, which isself-corrected by the Kalman Filter computation.

For every new time step “t” after the initial one (t=0), the KalmanFilter Gain is computed as: Equation  18:$K_{t} = \frac{\sigma_{t}}{\sigma_{t}^{2} + \sigma_{n}^{2}}$

With this Gain, the “updated” estimate of the signal is computed as:y _(u,t) =y _(t) +K _(t)(x _(t) −y _(t))  Equation 19

And the “updated” estimate of the variance is computed as:σ_(u,t) ²=(1−K _(t))σ_(t) ²  Equation 20

Before advancing to the next time step the estimate and the variance tothe newly “updated” values, are reset, i.e.,:y_(t+1)=y_(u,t)  Equation 21σ_(t+1) ²=σ_(u,t) ²  Equation 22

At this point the process is repeated for the next time step, startingwith Equation 18, until a desired or specified amount or level of noisereduction has been achieved.

The issue of arrival time adjustment can be handled in a variety ofways, as long as the field recordings are being retained in diskstorage. Since the simulation results are typically retained on diskalso, engineers can back-track the simulation to any point in time byusing a conventional “re-start” file. The present system then fetchesthe real-time information from the same time horizon and performs thenecessary comparisons.

It is to be noted that because of the different nature of real andsimulated data, one is not looking to match absolute values but ratherthe magnitude of changes in a property. Reservoir monitoring requiresonly a determination of the change in reservoir conditions from aprevious time horizon or step to the present time.

The simulated change is the difference between the monitored variable attwo different simulation times (fracture gradient, for example). Theactual change is the one computed by subtraction of the actual fieldmeasurements at the same two time horizons. If these changes agree, thesimulator is used to forecast what the future changes will be. Theengineer uses this forecast to determine how best to continue producingthe reservoir.

Measurement Technology and Surveillance Variables

The following table shows the correspondence between the continuousmeasurement technology applied and the variables that the simulatorcomputer 30 uses in matching these changes. The Equation numbers inparentheses in the following table correspond to the equation numberinglisted above: RESERVOIR MONITORING TECHNOLOGY VARIABLE SELECTED FORSURVEILLANCE Microseismic Horizontal Stress, Fracture Gradient andmonitoring Subsidence (Equations 14, 15, 16) Borehole Bulk Density(Equation 3) Gravimetry monitoring Time-Lapse (4D) Acoustic P-Impedanceand P-Reflectivity Seismic (Equations 8, 10) Multi-component ShearS-Impedance and S-Reflectivity Seismic (Equations 9, 11) Cross-Well TrueFormation Resistivity (Equation 17) Electromagnetics

Microseismic monitoring technology is unique in the group in the tableabove in the sense that only micro-earthquakes, and not true rockstress, can be measured in the field. These micro-earthquakes are not adirect measurement of either horizontal stress or fracture gradient orsubsidence but, instead, are a signal that some or all of these threemechanisms are actually occurring. Therefore, one compares these tremorswith changes in those three variables and determines if a directcorrelation exists.

The other reservoir monitoring technologies map directly into reservoirsimulation variables. Borehole gravimetry measures density changes.Therefore, the simulator can subtract the densities at the two timehorizons to determine change. Gravimetry is useful at tracking watersweeps in gas fields since the density difference between gas and wateris large enough to be accurately monitored. Oil fields can even bemonitored this way under certain circumstances (although the densitycontrast between oil and water is not as large).

Time-lapse 4D seismic monitoring measures changes in acoustic impedanceand reflectivity over time. Therefore the simulator's petro-elasticmodel can compute predicted changes in these two variables forcomparison. These changes are valuable indicators since the density andvelocity of sound propagation in oil, gas and water are all different.This can help determine if one fluid has displaced another duringproduction sweep.

Multi-component seismic monitoring measures changes in shear impedanceover time. Therefore the simulator's petro-elastic model during step 24can compute changes in this variable for comparison. Shear propertiesare sensitive to the density of the fluid contained therein.

Cross-well electromagnetics monitoring measures changes in trueformation resistivity. Therefore the simulator can compute changes inresistivity based on Archie's equation (17) for comparison. Resistivitychanges are indicators of hydrocarbon/water front movements sincehydrocarbons are electrically resistive while water is not.

Note that not all of the foregoing monitoring technologies areapplicable in a given field, as already discussed in the introductionsection. It is assumed that once a decision has been made to incur inthe expense of continuous monitoring, it is because sufficient modelinghas been done in advance to determine that the monitored variable isindeed sensitive to changes in the producing reservoir. Otherwise largesums of money could be spent in installing expensive monitoring hardwarewithout sufficient prior engineering analysis and modeling.

Displays of Monitoring Variables

FIGS. 9 through 13 are images of screen displays of three-dimensionalimages of certain of the monitoring variables for the same subsurfacehydrocarbon reservoir as that of FIGS. 5-8 at a selected depth andduring a given time step formed according to the present invention. Inthe displays of FIGS. 5-13, the reservoir is what is known as a giantreservoir, containing some 3×10¹¹ (or 300 billion) cubic meters involume and composed of some 500,000 cells and nine hydrocarboncomponents.

FIG. 9 is a screen display image of a three-dimensional fracturegradient for the reservoir determined in the petro-elastic modelaccording to Equation 15 above. As has been discussed, fracture gradientis related to microseismic monitoring. FIG. 10 is a screen display imageof horizontal stress for the reservoir determined in the petro-elasticmodel according to Equation 14 above. As has been discussed, horizontalstress is related to microseismic monitoring. FIG. 11 is a screendisplay image of uniaxial compaction determined in the petro-elasticmodel according to Equation 16 above. Uniaxial compaction is related tomicroseismic monitoring. FIG. 12 is a screen display image of bulkdensity determined in the petro-elastic model according to Equation 3above. Bulk density is related to borehole gravimetry monitoring. FIG.13 is a screen display image of P-wave impedance determined in thepetro-elastic model according to Equation 3 above. P-wave impedance isrelated to borehole 4D or time-lapse seismic monitoring.

The fracture gradient display of FIG. 9 is an indication ofrock-mechanical changes in the reservoir that may be associated withmicro-earthquakes (i.e. microseismic monitoring). The engineer willobserve this display in conjunction with the horizontal stress in thereservoir (FIG. 10). And use the rule below to scan for negative changesin horizontal stress (i.e. a decrease in the horizontal stress couldindicate that injected water has entered gas-filled rock pores, whichwere previously being compressed laterally by other fluid-bearing rock).Event Horizontal_Stress Always At_Well(All_Wells,2) Find_All Where(Gradient H_STRESS<0.0) Sound Talk(“Negative Stress Gradient At Well”,Well_Name) EndEvent

The uniaxial compaction display of FIG. 11 is an indicator of rocksubsidence. This most likely represents compression of the reservoir dueto its own weight by collapsing into vacant pores that were previouslyoccupied by oil, which was depleted due to oil production. This wouldgenerate micro-earthquakes that can be sensed by microseismicmonitoring. The engineer would set a proper alert monitor by thefollowing rule which is looking for subsidence effects greater than 2centimeters of vertical rock displacement or collapse: EventWell_Uniaxial_Compaction Always At_Well(All_Wells,2) Find_All Where(UNI_COMP>2.0) Sound Talk(“Uniaxial Compaction greater than 2 at well”,Well_Name) EndEvent

The bulk density display of FIG. 12 is used to observe density changesin the reservoir. These changes, if significant, could be measured byborehole gravimetry. An indication of a positive change (i.e. a gradientgreater than zero) reveals that a heavier fluid has displaced a lighterfluid at that location. This typically means that oil has been swept bya water front due to water injection. An event monitor rule would lookas follows: Event Bulk_Density2 Always At_Well(All_Wells,2) Find_AllWhere (Gradient BULKDENS>0.0) Sound Talk(“Density gradient greater thanzero at well”,Well_Name) EndEvent

The P-Wave impedance (or acoustic impedance) display of FIG. 13 isuseful to track seismic impedance changes due to fluid movement, such asa gas cap evolving from an oil reservoir due to decreased pressure. Butit is also useful to track locations where the impedance does notchange, which could be indicative of by-passed hydrocarbon that has notbeen reached by any of the producing wells and therefore suggest a newdrilling location. The case of gas cap evolution can be tracked by thefollowing event monitor rule: Event Impedance2 Always Find_All Where(Gradient P_IMPEDA<0) Sound Talk(“Negative Impedance Gradient at gridblock”, Cell_Location) EndEvent

Event Monitor Displays

FIGS. 14-25 are example schematic diagrams of reservoir cells adjacent awell or wells from the reservoir depicted in the data displays of FIGS.5-13. FIGS. 14-25 illustrate example results of event monitors accordingto the present invention. FIGS. 14-16 are example diagrams of eventmonitors according to the rule set forth below: Event CheckPressureAlways At_Layer ( 7 ) Find_All Where ( PRESSURE < 5000 ) Sound Play (“pr.wav” ) EndEventwhich occur at a defined reservoir depth or level for a keyword “Always”as defined above at three different times: t₁, t₂ and t₃ Each of thereservoir cells in the diagrams contains an indication of the followingmonitored variables: pressure (P); oil saturation (O), and watersaturation (W) of processing results in the manner set forth above. ForFIGS. 14-16 the syntax rule is that the pore pressure P must not be lessthan 5000 psia.

FIG. 14 illustrates at time t₁ that no alert is triggered because noreservoir cell pressure is less than the defined syntax rule of 5000psia. FIGS. 15 and 16 show, as indicated by hatching lines in threereservoir cells at each of times t₂ and t₃ that those three cellsexhibit the defined rule condition of the event monitor of pore pressurebeing less than 5000 psia. At simulation time t₂ and t₃ in this examplean audio or sound alert message is played to indicate a triggered alertand notify the reservoir engineer of the triggered alert.

FIGS. 17 and 18 are example diagrams of event monitors according to thefollowing rule: Event CheckPressure Once At_Layer ( 7 ) Find_All Where (PRESSURE < 5000 ) Sound Play ( “pr.wav” ) EndEventwhich occur for a keyword “Once” as defined above for the same the setsof reservoir cells as in FIGS. 14-16 at times t₁ and t₂ a defined syntaxrule of 5000 psia. As shown in FIG. 17 no alert is triggered because noreservoir cell pressure is less than the defined syntax rule of 5000psia. As shown in FIG. 18 by hatching lines in three reservoir cells attime t₂, those three cells exhibit the defined rule condition of theevent monitor of pore pressure being less than 5000 psia. An appropriatealarm for the reservoir engineer is formed at time t₂. Because of thealert at time t₂, and due to the keyword “Once”, no alert is triggeredat time t₃.

FIG. 19 is an example diagram of an event monitor according to the ruleset forth below: Event CheckPressure_and_watersat Always At_Layer ( 7 )Find_All Where ( ( PRESSURE < 5180 ) AND ( SWAT > 0.0) ) Sound Play (“pr.wav” ) Sound Play ( “wa.wav” ) EndEventwhich occurs for the keyword “Always” as defined above for the same thesets of reservoir cells as in FIGS. 14-16 and for a composite syntaxrule that: (1) pore pressure should not be less than 5180 psia and (2)water saturation should be some positive value greater than zero. Asshown in FIG. 19 by hatching lines in three reservoir cells at time t₃,those three cells exhibit the defined composite syntax rule conditionand a sound alert of the presence of each of the defined rule conditionsat those three cells is made. As shown in the diagrams of FIGS. 14 and15, the composite syntax rules are not met by the cells displayed inthese Figures and no alert is triggered at times t₁ and t₂.

FIG. 20 is an example diagram of an event monitor according to the ruleset forth below: Event CheckPressure_and_watersat_at_range AlwaysAt_Layer ( 7 ) Find_All Where (( PRESSURE (1:3, 2:4 , : ) < 5180 ) AND (SWAT( 1:3 , 2:4, : ) > 0.05 )) Sound Play ( “section.wav” ) EndEventwhich occurs for the keyword “Always” as defined but only for cells inthe range 1 to 3 in the x-direction, 2 to 4 in the y-direction for acomposite syntax rule that: (1) pore pressure should not be less than5180 psia and (2) water saturation should be greater than 0.05. As shownin FIG. 19 by hatching lines in three reservoir cells at time t₃, thosethree cells exhibit the defined composite syntax rule condition and asound alert of the presence of each of the defined rule conditions atthose three cells is made. As shown in the diagrams of FIGS. 14 and 15,the composite syntax rules are not met by the cells displayed in theseFigures and no alert is triggered at times t₁ and t₂.

FIG. 21 is an example plan diagram and FIG. 22 is a verticalcross-section diagram of the reservoir of an event monitor which occursfor the keyword “Always” applied at wells only (plus one cellneighboring the well) as defined above for a composite syntax rule that:(1) pore pressure should not be less than 5180 psia and (2) oilsaturation should be greater than 0.05. As shown in FIG. 21, only thewell cells and the immediate neighboring cells trigger an alarm in thiscase. Notice that this trigger applies to all layers from top to bottom(i.e. all well perforations) as shown in the cross-section in FIG. 22.

FIGS. 23, 24 and 25 are example diagrams of event monitors which occurfor a keyword “Always” as defined above for the same the sets ofreservoir cells as in FIGS. 14-16 at times t₁, t₂ and t₃ a definedsyntax rule of a pressure gradient of less than a negative 200 psia. Asshown in FIG. 23 no alert is triggered because no pressure gradientmeets the defined syntax rule. As shown in FIG. 24 by hatching lines ina single reservoir cell at time t₂, that cells exhibit the defined rulecondition of the event monitor of defined pressure gradient an alert istriggered. As shown in FIG. 25 by matching lines, a total of three cellswill trigger the prescribed alarm at time t₃.

The invention has been sufficiently described so that a person withaverage knowledge in the matter may reproduce and obtain the resultsmentioned in the invention herein Nonetheless, any skilled person in thefield of technique, subject of the invention herein, may carry outmodifications not described in the request herein, to apply thesemodifications to a determined structure, or in the manufacturing processof the same, requires the claimed matter in the following claims; suchstructures shall be covered within the scope of the invention.

It should be noted and understood that there can be improvements andmodifications made of the present invention described in detail abovewithout departing from the spirit or scope of the invention as set forthin the accompanying claims.

1. A computer-implemented method of calibrating a computerized reservoirmodel based on actual reservoir monitoring measurements obtained from asubsurface hydrocarbon reservoir, comprising the steps of: generatingnumerical predictions of reservoir variables in the computerizedreservoir model indicating predicted fluid and rock properties of thereservoir; converting the generated numerical reservoir variablepredictions into predicted sound sequences indicative of the generatednumerical reservoir variable predictions; storing the predicted soundsequences indicative of the generated numerical change predictions;converting actual reservoir monitoring measurements into actual soundsequences indicative of the actual reservoir monitoring measurements;storing the actual sound sequences; interactively comparing thepredicted sound sequences and the actual sound sequences at a selectedtime step to determine if adjustments in the computerized reservoirmodel are necessary.
 2. The computer-implemented method of claim 1,further including the step of: correcting the occurrence time of eventsindicative of changes in the fluid and rock properties of thecomputerized reservoir model predicted sound sequences and actual soundsequences to align the events in time.
 3. The computer-implementedmethod of claim 1, further including the step of: correcting theamplitude levels of events indicative of changes in the fluid and rockproperties of the reservoir in the predicted sound sequences and actualsound sequences to equalize the magnitude of the events.
 4. Thecomputer-implemented method of claim 1, further including the step of:applying Kalman filtering to the predicted sound sequences and actualsound sequences to minimize drift between the events.
 5. Thecomputer-implemented method of claim 1, further including the step of:repeating the step of comparing the predicted sound sequences and theactual sound sequences for other time steps than the selected time step.6. The computer-implemented method of claim 5, wherein at least one ofthe other time steps is earlier than the selected time step.
 7. Thecomputer-implemented method of claim 5, wherein at least one of theother time steps is later than the selected time step.
 8. Thecomputer-implemented method of claim 1, further including the step of:introducing changes in the computerized reservoir model based on theresults of the step of interactively comparing.
 9. Thecomputer-implemented method of claim 1, further including the steps of:digitally cross-correlating the predicted sound sequence and the actualsound sequence to determine the time shift correction needed and thegain or scale factor needed to equalize the magnitude of the predictedand actual renditions of a selected portion of the reservoir;interactively introducing changes in the computerized reservoir model tomatch the actual reservoir monitoring measurements; and repeating thesteps of digitally cross-correlating and interactively introducingchanges over a number of time windows of data until results of thedigital cross-correlation and the interactive comparison of soundsequences are acceptable.
 10. The computer-implemented method of claim1, wherein the reservoir monitoring measurements comprise: microseismicmonitoring measurements.
 11. The computer-implemented method of claim 1,wherein the reservoir monitoring measurements comprise: boreholegravimetry monitoring measurements.
 12. The computer-implemented methodof claim 1, wherein the reservoir monitoring measurements comprise:time-lapse seismic monitoring measurements.
 13. The computer-implementedmethod of claim 1, wherein the reservoir monitoring measurementscomprise: multi-component seismic monitoring measurements.
 14. Thecomputer-implemented method of claim 1, wherein the reservoir monitoringmeasurements comprise: cross-well electromagnetic monitoringmeasurements.
 15. The computer-implemented method of claim 1, whereinthe computer numerical predictions of reservoir variables include areservoir surveillance variable.
 16. The computer implemented method ofclaim 15, wherein the reservoir surveillance variable comprises: bulkdensity.
 17. The computer implemented method of claim 15, wherein thereservoir surveillance variable comprises: P-wave impedance.
 18. Thecomputer implemented method of claim 15, wherein the reservoirsurveillance variable comprises: P-wave reflectivity.
 19. The computerimplemented method of claim 15, wherein the reservoir surveillancevariable comprises: S-wave impedance.
 20. The computer implementedmethod of claim 15, wherein the reservoir surveillance variablecomprises: S-wave reflectivity.
 21. The computer implemented method ofclaim 10, wherein the reservoir surveillance variable comprises:horizontal stress.
 22. The computer implemented method of claim 10,wherein the reservoir surveillance variable comprises: fracturegradient.
 23. The computer implemented method of claim 10, wherein thereservoir surveillance variable comprises: true formation resistivity.24. The computer implemented method of claim 1, wherein the reservoirvolume is partitioned into a number of individual three-dimensionalcells.
 25. The computer implemented method of claim 24, wherein the stepof generating predicted values of properties of fluids comprises thestep of: generating a measure of the pressure in the individual cells ofthe reservoir.
 26. The computer implemented method of claim 25, whereinthe step of generating predicted values of properties of fluidscomprises the step of: generating a measure of the oil saturation in theindividual cells of the reservoir.
 27. The computer implemented methodof claim 25, wherein the step of generating predicted values ofproperties of fluids comprises the step of: generating a measure of thegas saturation in the individual cells of the reservoir.
 28. Thecomputer implemented method of claim 25, wherein the step of generatingpredicted values of properties of fluids comprises the step of:generating a measure of the water saturation in the individual cells ofthe reservoir.
 29. The computer implemented method of claim 25, whereinthe step of generating predicted values of properties of fluidscomprises the step of: generating a measure of the mole fractioncompositions in the individual cells of the reservoir.
 30. The computerimplemented method of claim 25, wherein the step of generating numericalpredictions of reservoir variables is performed over a projectedproduction life of the subsurface hydrocarbon reservoir.
 31. Thecomputer implemented method of claim 25, wherein the step of generatingnumerical predictions of reservoir variables is performed over a rangeof times during a projected production life of the subsurfacehydrocarbon reservoir.
 32. The computer-implemented method of claim 1,further including the step of: applying event monitoring rules to thegenerated numerical predictions of reservoir variables to detect whetherspecified event monitoring conditions are present at locations in thereservoir.
 33. The computer implemented method of claim 24, wherein theevent monitoring rules comprise a set of syntax rules relating toreservoir conditions.
 34. The computer implemented method of claim 24wherein the syntax rules of the event monitoring rules are composed inBackus Naur Form grammar.
 35. The computer implemented method of claim33, wherein the set of syntax rules include a syntax rule relating to areservoir surveillance variable.
 36. The computer implemented method ofclaim 33, wherein the set of syntax rules include a syntax rule relatingto a plurality of reservoir surveillance variables.
 37. The computerimplemented method of claim 33, wherein the set of syntax rules includea syntax rule relating to pressure in the individual cells of thereservoir.
 38. The computer implemented method of claim 33, wherein theset of syntax rules include a syntax rule relating to gas saturation inthe individual cells of the reservoir.
 39. The computer implementedmethod of claim 33, wherein the set of syntax rules include a syntaxrule relating to water saturation in the individual cells of thereservoir.
 40. The computer implemented method of claim 33, wherein theset of syntax rules include a syntax rule relating to data-minedbyproducts of reservoir surveillance variables.
 41. The computerimplemented method of claim 1, further including the step of storing theresults of the step of generating predicted values.
 42. A dataprocessing system for calibrating a computerized reservoir model basedon actual reservoir monitoring measurements obtained from a subsurfacehydrocarbon reservoir, comprising: a processor for performing the stepsof: generating numerical predictions of reservoir variables in thecomputerized reservoir model indicating predicted fluid and rockproperties of the reservoir; converting the generated numericalreservoir variable predictions into predicted digital sound sequencesindicative of the generated numerical reservoir variable predictions;storing the predicted digital sound sequences indicative of thegenerated numerical change predictions; converting actual reservoirmonitoring measurements into actual digital sound sequences indicativeof the actual reservoir monitoring measurements; storing the actualdigital sound sequences; interactively comparing the predicted digitalsound sequences and the actual digital sound sequences at a selectedtime step to determine if adjustments in the computerized reservoirmodel are necessary; a memory for storing the results of the steps ofstoring; and a computer audio output for playing audio versions of thepredicted digital sound sequences and the actual digital soundsequences.
 43. The data processing system of claim 42, wherein thereservoir volume is partitioned into a number of individualthree-dimensional cells.
 44. The data processing system of claim 42,wherein the processor further performs the step of: correcting theoccurrence time of events indicative of changes in the fluid and rockproperties of the computerized reservoir model predicted sound sequencesand actual sound sequences to align the events in time.
 45. The dataprocessing system of claim 42, further wherein the processor furtherperforms the step of: correcting the amplitude levels of eventsindicative of changes in the fluid and rock properties of the reservoirin the predicted sound sequences and actual sound sequences to equalizethe magnitude of the events.
 46. The data processing system of claim 42,further wherein the processor further performs the step of: applyingKalman filtering to the predicted sound sequences and actual soundsequences to minimize drift between the events.
 47. The data processingsystem of claim 42, further wherein the processor further performs thestep of: repeating the step of comparing the predicted sound sequencesand the actual sound sequences for other time steps than the selectedtime step.
 48. The data processing system of claim 42, further whereinthe processor further performs the step of: introducing changes in thecomputerized reservoir model based on the results of the step ofinteractively comparing.
 49. The data processing system of claim 42,further wherein the processor further performs the step of: digitallycross-correlating the predicted sound sequence and the actual soundsequence to determine the time shift correction needed and the gain orscale factor needed to equalize the magnitude of the predicted andactual renditions of a selected portion of the reservoir; interactivelyintroducing changes in the computerized reservoir model to match theactual reservoir monitoring measurements; and repeating the steps ofdigitally cross-correlating and interactively introducing changes over anumber of time windows of data until results of the digitalcross-correlation and the interactive comparison of sound sequences areacceptable.
 50. The data processing system of claim 42, wherein thereservoir monitoring measurements comprise: microseismic monitoringmeasurements.
 51. The data processing system of claim 42, wherein thereservoir monitoring measurements comprise: borehole gravimetrymonitoring measurements.
 52. The data processing system of claim 42,wherein the reservoir monitoring measurements comprise: time-lapseseismic monitoring measurements.
 53. The data processing system of claim42, wherein the reservoir monitoring measurements comprise:multi-component seismic monitoring measurements.
 54. Thecomputer-implemented method of claim 42, wherein the reservoirmonitoring measurements comprise: cross-well electromagnetic monitoringmeasurements.
 55. The data processing system of claim 42, wherein thecomputer numerical predictions of reservoir variables include areservoir surveillance variable.
 56. The data processing system of claim55 wherein the reservoir surveillance variable comprises: bulk density.57. The data processing system of claim 55 wherein the reservoirsurveillance variable comprises: P-wave impedance.
 58. The dataprocessing system of claim 55 wherein the reservoir surveillancevariable comprises: P-wave reflectivity.
 59. The data processing systemof claim 55 wherein the reservoir surveillance variable comprises:S-wave impedance.
 60. The data processing system of claim 55 wherein thereservoir surveillance variable comprises: S-wave reflectivity.
 61. Thedata processing system of claim 55 wherein the reservoir surveillancevariable comprises: horizontal stress.
 62. The data processing system ofclaim 55 wherein the reservoir surveillance variable comprises: fracturegradient.
 63. The data processing system of claim 55 wherein thereservoir surveillance variable comprises: true formation resistivity64. The data processing system of claim 42, wherein the reservoir volumeis partitioned into a number of individual three-dimensional cells. 65.The data processing system of claim 64, wherein the predicted values ofproperties of fluids generated by the processor comprise: a measure ofthe pressure in the individual cells of the reservoir.
 66. The dataprocessing system of claim 65, wherein the predicted values ofproperties of fluids generated by the processor comprise: a measure ofthe oil saturation in the individual cells of the reservoir.
 67. Thedata processing system of claim 65, wherein the predicted values ofproperties of fluids generated by the processor comprise: a measure ofthe gas saturation in the individual cells of the reservoir.
 68. Thedata processing system of claim 65, wherein the predicted values ofproperties of fluids generated by the processor comprise: a measure ofthe water saturation in the individual cells of the reservoir.
 69. Thedata processing system of claim 65, wherein the predicted values ofproperties of fluids generated by the processor comprise: a measure ofthe mole fraction compositions in the individual cells of the reservoir.70. The data processing system of claim 65, wherein the numericalpredictions of reservoir variables generated by the processor aregenerated over a range of times during a projected production life ofthe subsurface hydrocarbon reservoir.
 71. The data processing system ofclaim 65, wherein the numerical predictions of reservoir variablesgenerated by the processor are generated over a projected productionlife of the subsurface hydrocarbon reservoir.
 72. The data processingsystem of claim 42, wherein the processor further performs the step of:applying event monitoring rules to the generated numerical predictionsof reservoir variables to detect whether specified event monitoringconditions are present at locations in the reservoir.
 73. The dataprocessing system of claim 72, wherein the event monitoring rulesapplied by this processor comprise a set of syntax rules relating toreservoir conditions.
 74. The data processing system of claim 73 whereinthe syntax rules of the event monitoring rules are composed in BackusNaur Form grammar.
 75. The data processing system of claim 73, whereinthe set of syntax rules include a syntax rule relating to a reservoirsurveillance variable.
 76. The data processing system of claim 73,wherein the set of syntax rules include a syntax rule relating to aplurality of reservoir surveillance variables.
 77. The data processingsystem of claim 73, wherein the set of syntax rules include a syntaxrule relating to pressure in the individual cells of the reservoir. 78.The data processing system of claim 73, wherein the set of syntax rulesinclude a syntax rule relating to gas saturation in the individual cellsof the reservoir.
 79. The data processing system of claim 73, whereinthe set of syntax rules include a syntax rule relating to watersaturation in the individual cells of the reservoir.
 80. The dataprocessing system of claim 73, wherein the set of syntax rules include asyntax rule relating to oil saturation in the individual cells of thereservoir.
 81. The data processing system of claim 73, wherein the setof syntax rules include a syntax rule relating to data-mined byproductsof reservoir surveillance variables
 82. The data processing system ofclaim 42, wherein the processor further performs the step of storing thegenerated predicted values in the memory.
 83. A computer program storedin signal bearing media for causing a data processor to calibrate acomputerized reservoir model based on actual reservoir monitoringmeasurements obtained from a subsurface hydrocarbon reservoir thecomputer program product containing instructions stored inmachine-readable code and causing the processor to perform the followingsteps of: generating numerical predictions of reservoir variables in thecomputerized reservoir model indicating predicted fluid and rockproperties of the reservoir; converting the generated numericalreservoir variable predictions into predicted sound sequences indicativeof the generated numerical reservoir variable predictions; storing thepredicted sound sequences indicative of the generated numerical changepredictions; converting actual reservoir monitoring measurements intoactual sound sequences indicative of the actual reservoir monitoringmeasurements; storing the actual sound sequences; and interactivelycomparing the predicted sound sequences and the actual sound sequencesat a selected time step to determine if adjustments in the computerizedreservoir model are necessary.
 84. The computer program product of claim83, wherein the code instructions further include instructions whichcause the processor to perform the step of: correcting the occurrencetime of events indicative of changes in the fluid and rock properties ofthe computerized reservoir model predicted sound sequences and actualsound sequences to align the events in time.
 85. The computer programproduct of claim 83, wherein the code instructions further includeinstructions which cause the processor to perform the step of:correcting the amplitude levels of events indicative of changes in thefluid and rock properties of the reservoir in the predicted soundsequences and actual sound sequences to equalize the magnitude of theevents.
 86. The computer program product of claim 83, wherein the codeinstructions further include instructions which cause the processor toperform the step of: applying Kalman filtering to the predicted soundsequences and actual sound sequences to minimize drift between theevents.
 87. The computer program product of claim 83, wherein the codeinstructions further include instructions which cause the processor toperform the step of: repeating the step of comparing the predicted soundsequences and the actual sound sequences for other time steps than theselected time step.
 88. The computer program product of claim 83,wherein the code instructions further include instructions which causethe processor to perform the step of: introducing changes in thecomputerized reservoir model based on the results of the step ofinteractively comparing.
 89. The computer program product of claim 83,wherein the code instructions further include instructions which causethe processor to perform the step of: digitally cross-correlating thepredicted sound sequence and the actual sound sequence to determine thetime shift correction needed and the gain or scale factor needed toequalize the magnitude of the predicted and actual renditions of aselected portion of the reservoir; interactively introducing changes inthe computerized reservoir model to match the actual reservoirmonitoring measurements; and repeating the steps of digitallycross-correlating and interactively introducing changes over a number oftime windows of data until results of the digital cross-correlation andthe interactive comparison of sound sequences are acceptable.
 90. Thecomputer program product of claim 83, wherein the reservoir monitoringmeasurements comprise: microseismic monitoring measurements.
 91. Thecomputer program product of claim 83, wherein the reservoir monitoringmeasurements comprise: borehole gravimetry monitoring measurements. 92.The computer program product of claim 83, wherein the reservoirmonitoring measurements comprise: time-lapse seismic monitoringmeasurements.
 93. The computer program product of claim 83, wherein thereservoir monitoring measurements comprise: multi-component seismicmonitoring measurements.
 94. The computer program product of claim 83,wherein the reservoir monitoring measurements comprise: cross-wellelectromagnetic monitoring measurements.
 95. The computer programproduct of claim 83, wherein the computer numerical predictions ofreservoir variables include a reservoir surveillance variable.
 96. Thecomputer program product of claim 83, wherein the reservoir surveillancevariable comprises: bulk density.
 97. The computer program product ofclaim 83, wherein the reservoir surveillance variable comprises: P-waveimpedance.
 98. The computer program product of claim 83, wherein thereservoir surveillance variable comprises: P-wave reflectivity.
 99. Thecomputer program product of claim 83, wherein the code instructions forone step of generating predicted values of properties of fluids causethe processor to perform the step of: generating a measure of thesaturation in the individual cells of the reservoir.
 100. The computerprogram product of claim 83, wherein the code instructions for one stepof generating predicted values of properties of fluids cause theprocessor to perform the step of: generating a measure of the molefraction composition in the individual cells of the reservoir.
 101. Thecomputer program product of claim 83, wherein the code instructions forone step of generating predicted values of properties of fluids causethe processor to perform the step of: applying event monitoring rules tothe generated numerical predictions of reservoir variables to detectwhether specific event monitoring conditions are present at locations inthe reservoir.